Deformable Brain Registration Example: python

Packages and images

import os
import numpy
import pandas
import ants
import antspynet
outdir = "./outputPy/example/"
os.makedirs( outdir, exist_ok=True )
heavyLifting = 'SyNCC' # best results but costly - start with quick
quicker = 'SyN'
params = quicker
outprefix = outdir + 'examplePy-adniT1Template' + params
ch2 = ants.image_read( ants.get_data( "ch2") )
adniTemplate = ants.image_read( "./data/T_template0_BrainCerebellum.nii.gz")
adniTemplate = ants.crop_image( adniTemplate ).iMath_pad( 16 ).iMath("Normalize")
templateMask = ants.get_mask( adniTemplate, cleanup = 1 )
spc = ants.get_spacing( adniTemplate )
templateMaskVol = templateMask.sum() * numpy.prod( spc )
affq = ants.registration( ch2, adniTemplate, "QuickRigid" )

template image

ants.plot( adniTemplate, nslices=21, ncol=7,  crop=True, axis=2 )
#  domain_image_map = ( ch2, affq['fwdtransforms'] ) )

fn = "./data/sub-25659_ses-1_T1w.nii.gz"
img = ants.image_read( fn )
bxt = antspynet.brain_extraction( img ) # this is pre-computed so does not need to be run here
bxtThresh = ants.threshold_image( bxt, 0.25, 1e9 ).iMath("GetLargestComponent")
imgn3 = ants.n3_bias_field_correction( img * bxtThresh, 4 ).iMath("Normalize")
affq2 = ants.registration( ch2, imgn3, "QuickRigid" )

target image

ants.plot( imgn3, nslices=21, ncol=7,  crop=True, axis=2 )
# output the registration results to something like (last folder name in NRG style):
#  adniT1TemplateSyN  or adniT1TemplateSyNCC

reg = ants.registration( adniTemplate, imgn3, typeofTransform = params, outprefix=outprefix )

registered image

ants.plot( reg['warpedmovout'], nslices=21, ncol=7, axis=2, crop=True )

jacobian image

jacobian = ants.create_jacobian_determinant_image( adniTemplate, reg['fwdtransforms'][0], do_log = True, geom = True )
ants.plot( jacobian, nslices=21, ncol=7, axis=2 )

ants.image_write( jacobian, outprefix + "jacobian.nii.gz" )

edge map overlays

edgeDetect = ants.iMath( adniTemplate*255, "Canny", 1, 5, 12)
ants.plot( reg['warpedmovout'], ants.iMath( edgeDetect, "GD", 1 ), title = 'edge map',
  axis=2, ncol=7, nslices=21,  crop=True, overlay_cmap='viridis', overlay_alpha=0.5  )

summary of Results

affTx = ants.read_transform( reg['fwdtransforms'][1] )
paramvec = ants.get_ants_transform_parameters( affTx )[0:9]
affmat = numpy.reshape( paramvec, (3,3))
affVolChange = numpy.linalg.det( affmat )
summaryDF = { 'filename' : outprefix,
  'brainVol' : bxtThresh.sum() * numpy.prod( ants.get_spacing( imgn3 ) ),
  'affineVolumeChange' : affVolChange,
  'MI' : ants.image_mutual_information( adniTemplate , reg['warpedmovout'] ),
  'corr' : numpy.corrcoef( adniTemplate[templateMask>0] , reg['warpedmovout'][templateMask>0] )[0,1],
  'MSQ' : ( adniTemplate - reg['warpedmovout'] ).abs().mean() }
df = pandas.DataFrame([summaryDF])
df.to_csv( outprefix + "summary.csv" )
print( df )

##                                          filename   brainVol  affineVolumeChange       MI      corr       MSQ
## 0  ./outputPy/example/examplePy-adniT1TemplateSyN  1506955.0            1.019889 -0.65086  0.766179  0.021202